Machine learning techniques can be used to discover patterns in data, and to construct mathematical models based on the discovered patterns. The models can then be applied to make predictions on future data. Training a machine learning model can involve providing a machine learning algorithm with training data that includes a correct answer (e.g., a binary value, such as whether a machine part failed or not, whether a customer defaulted or not, etc.), which may be referred to as a target attribute. The machine learning algorithm discovers patterns in the training data that map input data attributes (e.g., other attributes of the machine part, the customer, etc.) to the target attribute (i.e., the answer to be predicted) and outputs a machine learning model that includes the patterns (e.g., a classifier).